Challenges exist in anatomical segmentation in medical imaging. Existing approaches including active shape models and active appearance models generate statistical models of a two-dimensional (2D) shape which can be deformed until the model matches an image. However, such approaches are restricted to the modality in which they are trained. In modalities where image content is low, such as projection x-ray or ultrasound, image ambiguity can lead to additional error in the model through error in the training data due to increased intra-observer and inter-observer variability.
Accordingly, a need exists for anatomical segmentation in medical imaging using a statistical model of anatomy built from detailed high-resolution imaging.